Growth Design of Stiffeners for Shell/Plate Structures Based on MADQN Interaction

Based on the Markov property of the growth steps of shell/plate stiffeners, a reinforcement learning driving strategy of the growth design of shell/plate stiffeners was proposed. Aiming at minimizing the overall strain energy of the structures, Markov decision process was used to model the growth processes of the stiffeners. By introducing a multi-agent system to share the states and the rewards of the stiffeners growth processes, and memorizing specific actions, the learning complexity was reduced. Meanwhile, the convergence of the reward value of the stiffeners growth processes was realized. Therefore, the growth design strategy of shell/plate stiffeners was achieved. Finally, a numerical example was given and the results of the smoothed stiffeners layout were compared with those of the classical algorithm, which verifies the validity of the growth design of stiffeners for shell / plate structures based on MADQN interaction. © 2024 Chinese Mechanical Engineering Society. All rights reserved.